Robin J Svensson (1), Katarina Niward (2, 3), Lina Davies Forsman (4, 5), Judith Bruchfeld (4, 5), Jakob Paues (2, 3), Thomas Schön (2, 6), Ulrika SH Simonsson (1)
(1) Department of Pharmaceutical Biosciences, Uppsala University, Sweden (2) Department of Clinical and Experimental Medicine, Linköping University, Sweden (3) Department of Infectious Diseases, Linköping University, Sweden (4) Department of Medicine Solna, Unit of Infectious Diseases, Karolinska Institutet, Sweden (5) Department of Infectious Diseases, Karolinska University Hospital Solna, Stockholm, Sweden (6) Department of Clinical Microbiology and Infectious Diseases, Kalmar County Hospital, Sweden
Objectives: This work evaluated a Bayesian model-based therapeutic drug monitoring (TDM) approach for rifampicin accounting for the known auto-induction and dose non-linearity in bioavailability.
Methods: Thirty-three patients treated for tuberculosis in Sweden were given rifampicin at 10 mg/kg as part of standard multi-drug therapy. Rifampicin plasma concentrations were measured at pre-dose and following 2, 4 and 6 hours post-dose after two weeks of treatment. Rifampicin plasma concentrations were also quantified at pre-dose and at 2 hours at weeks 4 and 12.
A new optimized TDM target for rifampicin of day 14 maximal concentration (Cmax, D14) of 35 mg/L was identified in a dose-ranging clinical trial using the observed geometric mean of Cmax,D14 following 35 mg/kg rifampicin, a dose which was found to have no safety concern [1]. Cmax was preferred over AUC as the target pharmacokinetics (PK), due to the post-antibiotic effect of rifampicin, where effect is most closely associated with Cmax and not AUC [2].
A Bayesian model-based TDM dose was derived for each patient by using a previous rifampicin PK model accounting for auto-induction and dose non-linearity in bioavailability for high dose rifampicin [3]. First, individual PK parameters for each patient was obtained through a Bayesian step using all individual data and the PK model using fixed and random parameters Inter-occasion variability was included in the estimation of individual PK parameters.
Secondly, the individual PK parameters were used to predict the Bayesian model-based Cmax,D14 following higher doses of 15-50 mg/kg. The highest dose not exceeding the TDM target Cmax,D14 of 35 mg/L, was predicted for each patient.
The simulated PK summary indices (Cmax and AUC0-24h) at day 14 were summarized for each dose level and compared with literature values [1] and related to the expected relative change in exposure if non-linear elimination was not present.
All estimations and simulations were performed in NONMEM 7.3 [4]. The M3 method was used to handle samples below the lower limit of quantification. A visual predictive check (VPC) was performed to evaluate the predictive properties of the model to describe the 10 mg/kg dose level.
Results: A total of 224 samples were included in the analysis of which 34.4% (77 samples) were below the lower limit of quantification. A VPC based on all the observed pharmacokinetic data stratified on day of treatment indicated that the model was able to describe the observed data well.
The Bayesian model-derived TDM dose in this patient population ranged from 20-45 mg/kg (mode 25 mg/kg) in order to meet the TDM target Cmax,D14 of 35 mg/L which is due to the higher clearance in this patient population compared to the population that was used to define the target [3].
The predicted values for Cmax and AUC0-24h at day 14 agreed well with published values of increasing doses of rifampicin indicating the similarity of this subpopulation compared to the subpopulation used to build the model [1, 3]. The predicted relative increase in AUC0-24h for 35 mg/kg compared with 10 mg/kg was 5.3 times higher, which is higher than to be expected under the assumption of linear PK.
Conclusion: Model-based TDM is warranted for dose optimization and personalised medicine for rifampicin due to its complex PK which makes predictions of individualized dosing impossible unless a model based approach is used. This study shows that the applied population PK model is optimal for TDM of rifampicin and that a new optimized TDM target of Cmax,D14 of 35 mg/L would lead to a dose with optimized effect on an individual patient level.
References:
[1] Boeree MJ, Diacon AH, Dawson R, Narunsky K, du Bois J, Venter A, Phillips PPJ, Gillespie SH, McHugh TD, Hoelscher M, Heinrich N, Rehal S, van Soolingen D, van Ingen J, Magis-Escurra C, Bruger D, Plemper van Balen G & Aarnoutse RE. A dose ranging trial to optimize the dose of rifampin in the treatment of tuberculosis. Am J Respir Crit Care Med 2015. 191(9): 1058-1065.
[2] Gumbo T, Louie A, Deziel MR, Liu W, Parsons LM, Salfinger M & Drusano GL. Concentration-dependent Mycobacterium tuberculosis killing and prevention of resistance by rifampin. Antimicob Agents Cehmother 2007. 51(11): 3781-3788.
[3] Svensson RJ, Aarnoutse RE, Diacon AH, Dawson R, Gillespie SH, Boeree MJ & Simonsson USH. A population pharmacokinetic model incorporating saturable pharmacokinetics and autoinduction for high rifampicin doses. Clin Pharmacol Ther 2017. doi: 10.1002/cpt.778
[4] Beal S, Sheiner LB, Boeckmann A & Bauer RJ. NONMEM Users Guides. 1989-2013. Icon Development Solutions, Ellicott City, Maryland, USA.
Reference: PAGE 27 (2018) Abstr 8698 [www.page-meeting.org/?abstract=8698]
Poster: Drug/Disease Modelling - Infection